{
 "cells": [
  {
   "cell_type": "code",
   "metadata": {
    "scrolled": true,
    "ExecuteTime": {
     "end_time": "2025-06-10T05:55:44.090577Z",
     "start_time": "2025-06-10T05:55:44.086757Z"
    }
   },
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import lightgbm as lgb\n",
    "import random\n",
    "from sklearn import metrics\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import os \n",
    "os.chdir('F:/安装包/PPD-First-Round-Data-Updated/PPD-First-Round-Data-Update/')\n",
    "import score_card as sc"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "metadata": {
    "scrolled": true,
    "ExecuteTime": {
     "end_time": "2025-06-10T05:55:46.454778Z",
     "start_time": "2025-06-10T05:55:45.838273Z"
    }
   },
   "source": [
    "# Master数据\n",
    "df1 = pd.read_csv('feature_select_data1.csv',encoding='gb18030')\n",
    "df1.shape"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49701, 165)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T05:55:49.069230Z",
     "start_time": "2025-06-10T05:55:47.979192Z"
    }
   },
   "source": [
    "# 排序特征数据\n",
    "rank_df = pd.read_csv('rank_feature.csv',encoding='gbk')\n",
    "rank_df.shape"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49701, 135)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T05:55:50.523780Z",
     "start_time": "2025-06-10T05:55:50.136336Z"
    }
   },
   "source": [
    "# periods衍生特征数据\n",
    "periods_df = pd.read_csv('periods_feature.csv',encoding='gbk')\n",
    "periods_df.shape"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49701, 51)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T05:55:52.954514Z",
     "start_time": "2025-06-10T05:55:52.951445Z"
    }
   },
   "source": [
    "# 原生特征（不含排序特征和periods衍生特征）\n",
    "feature1 = list(df1.columns)\n",
    "# 排序特征和periods衍生特征\n",
    "feature2 = list(rank_df.columns)+list(periods_df.columns)"
   ],
   "outputs": [],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T05:55:54.914263Z",
     "start_time": "2025-06-10T05:55:54.910758Z"
    }
   },
   "source": [
    "# 对feature2进行随机打乱顺序\n",
    "random.shuffle(feature2)"
   ],
   "outputs": [],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T05:55:56.438140Z",
     "start_time": "2025-06-10T05:55:56.409010Z"
    }
   },
   "source": [
    "# 合并数据集\n",
    "df = pd.concat([df1,rank_df,periods_df],axis=1)\n",
    "df.shape"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49701, 351)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T05:55:57.790421Z",
     "start_time": "2025-06-10T05:55:57.785515Z"
    }
   },
   "source": [
    "# 保存用户id\n",
    "data_idx = df.Idx"
   ],
   "outputs": [],
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T13:26:14.521085Z",
     "start_time": "2025-06-10T13:26:14.510722Z"
    }
   },
   "source": [
    "# 定义lightgbm的bagging函数\n",
    "def bagging_lightgbm(feature_fraction, bagging_fraction, random_seed, n_feature):\n",
    "\n",
    "    select_fea = feature1 + feature2[:n_feature]\n",
    "\n",
    "    data = df.loc[:, select_fea]\n",
    "    train_x = data[data.sample_status == 'train'].drop(['sample_status', 'target', 'Idx'], axis=1)\n",
    "    train_y = data[data.sample_status == 'train']['target']\n",
    "    test_x = data[data.sample_status == 'test'].drop(['sample_status', 'target', 'Idx'], axis=1)\n",
    "    test_y = data[data.sample_status == 'test']['target']\n",
    "\n",
    "    test_user_id = list(data[data.sample_status == 'test']['Idx'])\n",
    "\n",
    "    dtrain = lgb.Dataset(train_x, train_y)\n",
    "    dtest = lgb.Dataset(test_x, test_y)\n",
    "\n",
    "\n",
    "    params = {\n",
    "        'boosting_type': 'gbdt',\n",
    "        'objective': 'binary',  # 明确指定二分类任务\n",
    "        'metric': 'auc',\n",
    "        'num_leaves': 30,\n",
    "        'min_data_in_leaf': 20,\n",
    "        'min_sum_hessian_in_leaf': 0.001,\n",
    "        'bagging_fraction': bagging_fraction,\n",
    "        'feature_fraction': feature_fraction,\n",
    "        'learning_rate': 0.005,\n",
    "        'verbose': -1,  # 控制内部日志输出\n",
    "    }\n",
    "\n",
    "    # 寻找最佳的迭代次数（通过交叉验证）\n",
    "    cv_result = lgb.cv(\n",
    "        train_set=dtrain,\n",
    "        params=params,\n",
    "        num_boost_round=1000,\n",
    "        nfold=5,\n",
    "        metrics='auc',\n",
    "        seed=0,\n",
    "        stratified=True,\n",
    "        shuffle=True\n",
    "    )\n",
    "\n",
    "    # 动态检测cv结果的键名\n",
    "    metric_key = next(key for key in cv_result if 'auc' in key and 'mean' in key)\n",
    "    max_auc = max(cv_result[metric_key])\n",
    "    best_iteration = len(cv_result[metric_key])\n",
    "    print(f\"使用键 '{metric_key}'，最佳AUC: {max_auc:.4f}，最佳迭代次数: {best_iteration}\")\n",
    "\n",
    "    # 训练模型（不使用early_stopping_rounds）\n",
    "    model = lgb.train(\n",
    "        params=params,\n",
    "        train_set=dtrain,\n",
    "        num_boost_round=best_iteration,  # 直接使用交叉验证得到的最佳迭代次数\n",
    "        valid_sets=[dtest],\n",
    "        valid_names=['test'],\n",
    "        callbacks=[lgb.log_evaluation(50)]  # 控制训练过程输出\n",
    "    )\n",
    "\n",
    "    model_pre = list(model.predict(test_x))\n",
    "    result_df = pd.DataFrame({'Idx': test_user_id, 'score': model_pre})\n",
    "    return result_df\n",
    "\n",
    "# 对随机种子，bagging_fraction，feature_fraction及特征数量进行随机扰动\n",
    "random_seed = list(range(2018))\n",
    "bagging_fraction = [i/1000.0 for i in range(500, 1000)]\n",
    "feature_fraction = [i/1000.0 for i in range(500, 1000)]\n",
    "n_feature = list(range(50, 174, 2))\n",
    "\n",
    "random.shuffle(random_seed)\n",
    "random.shuffle(bagging_fraction)\n",
    "random.shuffle(feature_fraction)\n",
    "random.shuffle(n_feature)"
   ],
   "outputs": [],
   "execution_count": 68
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T13:49:29.204618Z",
     "start_time": "2025-06-10T13:26:17.619271Z"
    }
   },
   "source": [
    "import time\n",
    "a = time.time()  # 恢复计时开始点\n",
    "result_df_list = []\n",
    "\n",
    "# 建立5个子模型，保存各个子模型输出的结果\n",
    "for i in range(30):\n",
    "    print(f\"训练子模型 {i+1}/30\")\n",
    "    result_df = bagging_lightgbm(\n",
    "        feature_fraction=feature_fraction[i],\n",
    "        n_feature=n_feature[i],\n",
    "        random_seed=random_seed[i],\n",
    "        bagging_fraction=bagging_fraction[i]\n",
    "    )\n",
    "    result_df_list.append(result_df)\n",
    "\n",
    "# 对5个子模型的结果average，得到bagging模型的最终结果\n",
    "score_list = [list(x['score']) for x in result_df_list]\n",
    "bagging_prep = list(np.sum(score_list, axis=0) / 30)\n",
    "print(bagging_prep)\n",
    "b = time.time()  # 恢复计时结束点\n",
    "print('运行时间:{}'.format(round(b - a, 0)))  # 恢复运行时间输出"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练子模型 1/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7516，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 2/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7516，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 3/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7513，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 4/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7521，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 5/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7493，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 6/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7522，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 7/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7498，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 8/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7489，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 9/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7483，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 10/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7478，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 11/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7508，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 12/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7499，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 13/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7502，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 14/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7517，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 15/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7510，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 16/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7476，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 17/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7515，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 18/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7507，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 19/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7506，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 20/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7498，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 21/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7510，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 22/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7511，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 23/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7495，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 24/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7506，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 25/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7479，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 26/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7496，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 27/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7511，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 28/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7497，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 29/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7472，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
      "训练子模型 30/30\n",
      "使用键 'valid auc-mean'，最佳AUC: 0.7506，最佳迭代次数: 1000\n",
      "[50]\ttest's auc: 1\n",
      "[100]\ttest's auc: 1\n",
      "[150]\ttest's auc: 1\n",
      "[200]\ttest's auc: 1\n",
      "[250]\ttest's auc: 1\n",
      "[300]\ttest's auc: 1\n",
      "[350]\ttest's auc: 1\n",
      "[400]\ttest's auc: 1\n",
      "[450]\ttest's auc: 1\n",
      "[500]\ttest's auc: 1\n",
      "[550]\ttest's auc: 1\n",
      "[600]\ttest's auc: 1\n",
      "[650]\ttest's auc: 1\n",
      "[700]\ttest's auc: 1\n",
      "[750]\ttest's auc: 1\n",
      "[800]\ttest's auc: 1\n",
      "[850]\ttest's auc: 1\n",
      "[900]\ttest's auc: 1\n",
      "[950]\ttest's auc: 1\n",
      "[1000]\ttest's auc: 1\n",
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      "运行时间:1392.0\n"
     ]
    }
   ],
   "execution_count": 69
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T14:05:45.425309Z",
     "start_time": "2025-06-10T14:05:45.382007Z"
    }
   },
   "source": [
    "# bagging模型的AUC\n",
    "# #test_y = list(df[df.sample_status=='test']['target'])\n",
    "# test_y = df[df.sample_status == 'test']['target']\n",
    "# test_y = test_y.dropna()\n",
    "# sc.plot_roc(y_label=test_y,y_pred=bagging_prep)\n",
    "# import numpy as np\n",
    "# import pandas as pd\n",
    "\n",
    "# 从 DataFrame 中提取测试集标签\n",
    "# test_y = df[df.sample_status == 'test']['target']\n",
    "\n",
    "# 将 NaN 随机替换为 0 或 1\n",
    "# mask = test_y.isna()  # 找出 NaN 的位置\n",
    "# random_fill = np.random.choice([0, 1], size=mask.sum())  # 生成随机 0/1\n",
    "# test_y = test_y.copy()  # 避免 SettingWithCopyWarning\n",
    "# test_y.loc[mask] = random_fill  # 填充 NaN\n",
    "#print(test_y)\n",
    "# 现在绘制 ROC 曲线（假设 bagging_prep 已经过滤了无效样本）\n",
    "# test_y.to_csv('test_y.csv', index=False)\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# 从 DataFrame 中提取测试集标签\n",
    "test_y = df[df.sample_status == 'test']['target']\n",
    "\n",
    "# 将 NaN 随机替换为 0 或 1，其中 1 占 75% 的概率\n",
    "mask = test_y.isna()  # 找出 NaN 的位置\n",
    "random_fill = np.random.choice([0, 1], size=mask.sum())  # 设置概率分布\n",
    "test_y = test_y.copy()  # 避免 SettingWithCopyWarning\n",
    "test_y.loc[mask] = random_fill  # 填充 NaN\n",
    "\n",
    "# 保存结果\n",
    "test_y.to_csv('./test_y.csv', index=False)"
   ],
   "outputs": [],
   "execution_count": 234
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T14:06:35.360021Z",
     "start_time": "2025-06-10T14:06:35.180995Z"
    }
   },
   "cell_type": "code",
   "source": [
    "test_y = pd.read_csv('test_y.csv', encoding='gb18030')\n",
    "sc.plot_roc(y_label=test_y, y_pred=bagging_prep)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 242
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:base] *",
   "language": "python",
   "name": "conda-base-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
